Analytical and experimental evaluation of cluster-based network servers

Abstract

In this paper we use analytic modeling and simulation to evaluate network servers implemented on clusters of workstations. More specifically, we model the potential benefits of locality-conscious request distribution within the cluster and evaluate the performance of a cluster-based server (called L2S) we designed in light of our experience with the model. Our most important modeling results show that locality-conscious distribution on a 16-node cluster can increase server throughput with respect to a locality-oblivious server by up to 5-fold, depending on the average size of the files requested and on the size of the server's working set. Our simulation results demonstrate that L2S achieves throughput that is within 28% of the full potential of locality-conscious distribution on 16 nodes, outperforming and significantly outscaling the best-known locality-conscious server. Based on our results and on the fact that the files serviced by network servers are becoming larger and more numerous, we conclude that our locality-conscious network server should prove very useful for its performance, scalability, and availability properties.